## Contents

%matplotlib inline
import numpy as np
from scipy import stats
from scipy.stats import norm
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('white')
sns.set_context('paper')
import pandas as pd


## How do co-ordinates transform?

The transformation from cartesian-to-polar co-ordinates is simple but interesting.

which, more familiarly gives:

Both of these are non-linear transforms as can be seen below.

We first show the $(x,y) \rightarrow (r, \theta)$ transform:

rf = lambda x,y: np.sqrt(x*x + y*y)
thetaf = lambda x,y: np.arctan(y/x)
plt.figure(figsize=[12,6])
ax1 = plt.subplot(1,2,1)
ax2 = plt.subplot(1,2,2)
ax1.set_xlim([0, 1/2])
ax1.set_ylim([0, 1/2])
gsize=20
for pt in np.linspace(0, 1/2, gsize):
x = np.array([pt]*gsize)
y = np.linspace(0, 1/2, gsize)
ax1.plot(x, y, color="r")
r = rf(x,y)
theta = thetaf(x,y)
ax2.plot(r, theta, color="r")
x = np.linspace(0, 1/2, gsize)
y = np.array([pt]*gsize)
ax1.plot(x, y, color="b")
r = rf(x,y)
theta = thetaf(x,y)
ax2.plot(r, theta, color="b")
testx = np.array([0.4, 0.4, 0.25, 0.01, 0.45])
testy = np.array([0.01, 0.1, 0.25, 0.4, 0.45])
ax1.plot(testx, testy, 'o', color='g')
ax2.plot(rf(testx, testy), thetaf(testx, testy), 'o', color='g')

[<matplotlib.lines.Line2D at 0x11b39ce80>]


And you can see stuff in the upper right quadrant transform more linearly…because its smaller

rf = lambda x,y: np.sqrt(x*x + y*y)
thetaf = lambda x,y: np.arctan(y/x)
xf = lambda r, theta: r*np.cos(theta)
yf = lambda r, theta: r*np.sin(theta)
plt.figure(figsize=[12,6])
ax1 = plt.subplot(1,2,1)
ax2 = plt.subplot(1,2,2)
#ax2.set_xlim([0, 0.5])
#ax2.set_ylim([0, 0.5])
gsize=10
for pt in np.linspace(0, 0.7, gsize):
r = np.array([pt]*gsize)
theta = np.linspace(0, 0.8, gsize)
ax1.plot(r, theta, color="r")
x = xf(r,theta)
y = yf(r, theta)
ax2.plot(x, y, color="r")
for pt in np.linspace(0, 0.8, gsize*2):
r = np.linspace(0, 0.7, gsize*2)
theta = np.array([pt]*gsize*2)
ax1.plot(r, theta, color="b")
x = xf(r,theta)
y = yf(r, theta)
ax2.plot(x, y, color="b")


Even with generally non-linear transforms, local stuff transforms linearly. You can see this from the chain rule.

Then the infinitessimal areas transform via the determinant of the partials of this transform, or the determinantJacobian $J$, which can be written:

(or its transpose, depending on whether we want to consider vectors as row vectors or column vectors).

Thus:

## How do probabilities transform?

What is $f_{R,\Theta}(r,\theta)$?

In general:

Let $z=g(x)$ so that $x=g^{-1}(z)$

Define the Jacobian $J(z)$ of the transformation $x=g^{-1}(z)$ as the above partial derivatives matrix of the transformation.

Then:

Let $g$ : $r=\sqrt{x^2 + y^2}$, $\theta = arctan(y/x)$. Then $g^{-1}$ : $x=r\,cos(\theta)$, $y=r\,sin(\theta)$

$= \frac{1}{\sqrt{2\pi}} e^{-(r cos(\theta))^2/2} \times \frac{1}{\sqrt{2\pi}} e^{-(r sin(\theta))^2/2} = \frac{1}{2\pi} \times e^{-r^2/2} \times r$.

which is a Raleigh distribution.